The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Advancements in the field of image processing have led to development of various algorithms for detection and monitoring of one or more objects in the field-of-view of a camera. Such systems may be deployed in collision avoidance systems, security cameras, sensor dirt detection, environment pollution analysis, and the like. In certain scenarios, one or more contaminants such as dirt, water, droplets, mud, leaves, oil deposits, smudge, and the like may be present on the lens of the imaging device. This may lead to degradation of the performance of the image by blurring of the output image, false detections in the output image, and the like. Such degradation in performance can prove to be fatal in cases the image processing is being performed for sensitive applications, such as a collision avoidance system.
Various techniques have been developed to detect the contaminants present on the imaging device. In accordance with one of the current techniques, the detection of the contaminant is based on a contrast difference and an interframe difference of the one or more image frames captured by the imaging device. The technique fails to differentiate between various types of contaminants. In accordance with another technique, a plurality of imaging devices is used for detection of the contaminants that increases the hardware requirement and complexity. In accordance with another technique, difference image is used to detect a raindrop that is transparent in nature. The technique, however, does not consider the background information that is transmitted through the raindrop. Therefore, the technique is not preferred for detection of transparent contaminants whose luminance depends on the background information. In accordance with another technique, the captured image is considered to be a composite wave, wherein the presence of the contaminant changes the property of composite wave. An image frequency power is used for the detection of the change in the property of the composite wave. In accordance with another technique, a difference of frames between input frames is determined for detection of dark and bright spots. Further, a temporal analysis is performed on the one or more frames to detect the contaminants.
The existing techniques for contaminant detection typically require a large number of image frames to accurately detect the contaminant and are slow and computationally expensive. Therefore, there is a need for a technique that minimises the computation required for accurately detecting contaminant present on the lens of the imaging device. Further, it is desirable that the computation for detection of contaminant is performed in real-time. Additionally, it may be required to reduce the hardware required for implementing such a process so as to make the system deployable across mobile platforms.